The exponential growth of databases that contains biological information (such as protein and DNA data) demands great efforts to improve the performance of computational platforms. In this work we investigate how bioinformatics applications benefit from parallel architectures that combine different alternatives to exploit coarseand
fine-grain parallelism. As a case of analysis we study the performance behavior of the search application that implements the
Smith-Waterman algorithm, which is a dynamic programing approach that explores the similarity between a pair of sequences.
The inherent large parallelism of the algorithm makes it ideal for architectures supporting multiple dimensions of parallelism (TLP, DLP and ILP). We study how this algorithm can take advantage of
different parallel machines like the SGI Altix, IBM Power6, Cell BE machines and MareNostrum. Our results show that a share
memory architecture like the PowerPC 970MP of Marenostrum can surpass a heterogeneous machine like the current Cell BE. Our
quantitative analysis includes not only a study of scalability of the performance in terms of speedup, but also includes the analysis of bottlenecks in the execution of the application. This analysis is carried out through the study of the execution phases that the application presents.